Recently, fair AI has also attracted attention as a crucial machine learning research topic.
Real-world datasets may have unexpected biases, and many researchers are working hard to eliminate such biases in machine-learning models.
Fortunately, owing to the modularity of NAM [1]}, removing biases from trained HONAM is easy.
We show that a HONAM trained on the COMPAS recidivism dataset is biased and introduce a method for eliminating biases.
